{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Replicating_Iris_Dataset.ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyMj6rBRxnOds8b0NasYi1oP"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"code","metadata":{"id":"DcGbHYeE2eGr","colab_type":"code","colab":{}},"source":["#da Iris estraiamo mean, sd e ricreiamo un dataset fittizio misurandone l'accuratezza\n","X = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data')"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"U3We8sWs8hWj","colab_type":"code","outputId":"8a63cef3-8361-4c4d-b2ca-496d6d5ef630","executionInfo":{"status":"ok","timestamp":1588526109658,"user_tz":-120,"elapsed":1485,"user":{"displayName":"Michelangiolo Mazzeschi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghg8l8QeAQA6fd1iPVc2f_F0XQpum7zRjrF4R7HRw=s64","userId":"15738155355555420240"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["X.columns = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']\n","X.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>sepal_length</th>\n","      <th>sepal_width</th>\n","      <th>petal_length</th>\n","      <th>petal_width</th>\n","      <th>species</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>4.9</td>\n","      <td>3.0</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>4.7</td>\n","      <td>3.2</td>\n","      <td>1.3</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>4.6</td>\n","      <td>3.1</td>\n","      <td>1.5</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>5.0</td>\n","      <td>3.6</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5.4</td>\n","      <td>3.9</td>\n","      <td>1.7</td>\n","      <td>0.4</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   sepal_length  sepal_width  petal_length  petal_width      species\n","0           4.9          3.0           1.4          0.2  Iris-setosa\n","1           4.7          3.2           1.3          0.2  Iris-setosa\n","2           4.6          3.1           1.5          0.2  Iris-setosa\n","3           5.0          3.6           1.4          0.2  Iris-setosa\n","4           5.4          3.9           1.7          0.4  Iris-setosa"]},"metadata":{"tags":[]},"execution_count":20}]},{"cell_type":"code","metadata":{"id":"mYmOPG4q-sJD","colab_type":"code","colab":{}},"source":["y = X.pop('species')\n","y = pd.DataFrame(y)\n","columns = X.columns"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"lvh_tmaF9JCS","colab_type":"code","outputId":"df5ffe93-293f-4a0b-db7c-7d4417415df5","executionInfo":{"status":"ok","timestamp":1588526117358,"user_tz":-120,"elapsed":2725,"user":{"displayName":"Michelangiolo Mazzeschi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghg8l8QeAQA6fd1iPVc2f_F0XQpum7zRjrF4R7HRw=s64","userId":"15738155355555420240"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["X.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>sepal_length</th>\n","      <th>sepal_width</th>\n","      <th>petal_length</th>\n","      <th>petal_width</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>4.9</td>\n","      <td>3.0</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>4.7</td>\n","      <td>3.2</td>\n","      <td>1.3</td>\n","      <td>0.2</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>4.6</td>\n","      <td>3.1</td>\n","      <td>1.5</td>\n","      <td>0.2</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>5.0</td>\n","      <td>3.6</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5.4</td>\n","      <td>3.9</td>\n","      <td>1.7</td>\n","      <td>0.4</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   sepal_length  sepal_width  petal_length  petal_width\n","0           4.9          3.0           1.4          0.2\n","1           4.7          3.2           1.3          0.2\n","2           4.6          3.1           1.5          0.2\n","3           5.0          3.6           1.4          0.2\n","4           5.4          3.9           1.7          0.4"]},"metadata":{"tags":[]},"execution_count":22}]},{"cell_type":"code","metadata":{"id":"Dvfg6g-d9rEg","colab_type":"code","outputId":"c1eeeb40-143c-4ad0-deba-399130bd3044","executionInfo":{"status":"ok","timestamp":1588525904506,"user_tz":-120,"elapsed":1223,"user":{"displayName":"Michelangiolo Mazzeschi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghg8l8QeAQA6fd1iPVc2f_F0XQpum7zRjrF4R7HRw=s64","userId":"15738155355555420240"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["from sklearn.model_selection import train_test_split\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=11)\n","print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["(119, 4) (30, 4) (119, 1) (30, 1)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"32TK-Psa9m0g","colab_type":"code","outputId":"a960554b-d39c-474c-de5e-712312f91999","executionInfo":{"status":"ok","timestamp":1588525906141,"user_tz":-120,"elapsed":1175,"user":{"displayName":"Michelangiolo Mazzeschi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghg8l8QeAQA6fd1iPVc2f_F0XQpum7zRjrF4R7HRw=s64","userId":"15738155355555420240"}},"colab":{"base_uri":"https://localhost:8080/","height":68}},"source":["#misuriamo l'accuratezza del dataset reale\n","#Gaussian Naive Bayes\n","import numpy as np\n","from sklearn.naive_bayes import GaussianNB\n","\n","#training the AI\n","clf = GaussianNB()\n","clf.fit(X_train, y_train)\n","print(clf.score(X_test, y_test))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["0.9333333333333333\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/sklearn/naive_bayes.py:206: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n","  y = column_or_1d(y, warn=True)\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"60PLWHAr-cJc","colab_type":"code","colab":{}},"source":["#we create the list of sd\n","from math import *\n","var = list(clf.sigma_) #variance\n","var_list = list()\n","\n","for i in range(len(var)):\n","  for j in range(0, 4):\n","    var_list.append(var[i][j])\n","\n","for i in range(len(var_list)):\n","  var_list[i] = sqrt(var_list[i])\n","sd = var_list\n","\n","#we create the list of mean\n","mean = clf.theta_ #mean\n","mean_list = list()\n","for i in range(len(mean)):\n","  for j in range(0, 4):\n","    mean_list.append(mean[i][j])\n","mean = mean_list\n","\n","#we create the list of alpha\n","alpha = [0]*12\n","\n","#we merge them all togethere to fit the pattern: [mean, sd, alpha]\n","features = [[0 for x in range(3)] for x in range(12)]\n","for r in range(len(sd)):\n","  features[r][0] = mean[r]\n","  features[r][1] = sd[r]\n","  features[r][2] = alpha[r]"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"QBbeaE5-Arns","colab_type":"code","outputId":"5649a041-f47b-4d36-a0d7-6674c060dd91","executionInfo":{"status":"ok","timestamp":1588458033106,"user_tz":-120,"elapsed":963,"user":{"displayName":"Michelangiolo Mazzeschi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghg8l8QeAQA6fd1iPVc2f_F0XQpum7zRjrF4R7HRw=s64","userId":"15738155355555420240"}},"colab":{"base_uri":"https://localhost:8080/","height":221}},"source":["features"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[[5.0175, 0.33607105376489926, 0],\n"," [3.432500000000001, 0.39584561785960176, 0],\n"," [1.4774999999999996, 0.17390731203330645, 0],\n"," [0.2399999999999999, 0.099498759683977, 0],\n"," [5.9526315789473685, 0.5003876645292117, 0],\n"," [2.7263157894736842, 0.31007996979196273, 0],\n"," [4.2289473684210535, 0.45474567836045904, 0],\n"," [1.3052631578947371, 0.1834571145361789, 0],\n"," [6.636585365853658, 0.6565824689818087, 0],\n"," [3.0, 0.33129458302185594, 0],\n"," [5.597560975609757, 0.5849693806430326, 0],\n"," [2.0487804878048776, 0.2733013683095554, 0]]"]},"metadata":{"tags":[]},"execution_count":151}]},{"cell_type":"code","metadata":{"id":"kZCHl0aI0GGc","colab_type":"code","colab":{}},"source":["labels = list(y['species'].unique())\n","names = [[X.columns[x]+' '+labels[m]] for m in range(len(labels)) for x in range(len(X.columns))]"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"gSUKrN1bC-TH","colab_type":"code","outputId":"2bee2044-c71a-4f23-ca86-fdcddfece5f0","executionInfo":{"status":"ok","timestamp":1588458175208,"user_tz":-120,"elapsed":978,"user":{"displayName":"Michelangiolo Mazzeschi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghg8l8QeAQA6fd1iPVc2f_F0XQpum7zRjrF4R7HRw=s64","userId":"15738155355555420240"}},"colab":{"base_uri":"https://localhost:8080/","height":221}},"source":["names"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[['sepal_length Iris-setosa'],\n"," ['sepal_width Iris-setosa'],\n"," ['petal_length Iris-setosa'],\n"," ['petal_width Iris-setosa'],\n"," ['sepal_length Iris-versicolor'],\n"," ['sepal_width Iris-versicolor'],\n"," ['petal_length Iris-versicolor'],\n"," ['petal_width Iris-versicolor'],\n"," ['sepal_length Iris-virginica'],\n"," ['sepal_width Iris-virginica'],\n"," ['petal_length Iris-virginica'],\n"," ['petal_width Iris-virginica']]"]},"metadata":{"tags":[]},"execution_count":168}]},{"cell_type":"code","metadata":{"id":"w4I2Ca30-YqJ","colab_type":"code","colab":{}},"source":["#generiamo le distribuzioni partendo dai dati\n","X2, y2, all_norm_copy = algo(features, labels, 1000000) #features devono essere sotto forma di sd, mean, alpha"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"oh2Ohbhx02Mg","colab_type":"code","outputId":"dc11dc36-842c-40e6-a42f-1c1ec4dbf185","executionInfo":{"status":"ok","timestamp":1588526133149,"user_tz":-120,"elapsed":691,"user":{"displayName":"Michelangiolo Mazzeschi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghg8l8QeAQA6fd1iPVc2f_F0XQpum7zRjrF4R7HRw=s64","userId":"15738155355555420240"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["#entire dataset\n","X2.reset_index(drop=True, inplace=True)\n","X2.columns = columns\n","y2.reset_index(drop=True, inplace=True)\n","y2.columns = ['species']\n","a = pd.concat([X2, y2], axis=1) #se non resettiamo gli index da errore\n","a.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>sepal_length</th>\n","      <th>sepal_width</th>\n","      <th>petal_length</th>\n","      <th>petal_width</th>\n","      <th>species</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>5.441639</td>\n","      <td>3.162089</td>\n","      <td>1.248066</td>\n","      <td>0.162619</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>4.794150</td>\n","      <td>2.926283</td>\n","      <td>1.163067</td>\n","      <td>0.200445</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>4.980443</td>\n","      <td>3.654110</td>\n","      <td>1.719066</td>\n","      <td>0.188494</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4.900419</td>\n","      <td>2.793317</td>\n","      <td>1.396354</td>\n","      <td>0.219312</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4.746637</td>\n","      <td>3.337939</td>\n","      <td>1.356126</td>\n","      <td>0.045387</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   sepal_length  sepal_width  petal_length  petal_width      species\n","0      5.441639     3.162089      1.248066     0.162619  Iris-setosa\n","1      4.794150     2.926283      1.163067     0.200445  Iris-setosa\n","2      4.980443     3.654110      1.719066     0.188494  Iris-setosa\n","3      4.900419     2.793317      1.396354     0.219312  Iris-setosa\n","4      4.746637     3.337939      1.356126     0.045387  Iris-setosa"]},"metadata":{"tags":[]},"execution_count":24}]},{"cell_type":"code","metadata":{"id":"U3lQDqWz0-Sb","colab_type":"code","outputId":"6cee04ec-fb4f-451b-bd29-a335cbfb1457","executionInfo":{"status":"ok","timestamp":1588451932210,"user_tz":-120,"elapsed":1174,"user":{"displayName":"Michelangiolo Mazzeschi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghg8l8QeAQA6fd1iPVc2f_F0XQpum7zRjrF4R7HRw=s64","userId":"15738155355555420240"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["from sklearn.model_selection import train_test_split\n","X2_train, X2_test, y2_train, y2_test = train_test_split(X, y, test_size=0.2, random_state=11)\n","print(X2_train.shape, X2_test.shape, y2_train.shape, y2_test.shape)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["(2400000, 4) (600000, 4) (2400000, 1) (600000, 1)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Q4rlB80u-6QK","colab_type":"code","outputId":"f9997381-4706-42ce-f7aa-5045fd0d4766","executionInfo":{"status":"ok","timestamp":1588526141799,"user_tz":-120,"elapsed":5752,"user":{"displayName":"Michelangiolo Mazzeschi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghg8l8QeAQA6fd1iPVc2f_F0XQpum7zRjrF4R7HRw=s64","userId":"15738155355555420240"}},"colab":{"base_uri":"https://localhost:8080/","height":68}},"source":["#misuriamo accuratezza delle nostre approssimazioni\n","#Gaussian Naive Bayes\n","import numpy as np\n","from sklearn.naive_bayes import GaussianNB\n","\n","#training the AI\n","clf = GaussianNB()\n","clf.fit(X2, y2)\n","print(clf.score(X, y))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/sklearn/naive_bayes.py:206: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n","  y = column_or_1d(y, warn=True)\n"],"name":"stderr"},{"output_type":"stream","text":["0.9530201342281879\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Uke8bK57gIGe","colab_type":"code","outputId":"60fd3139-bbdd-4379-a720-7e2273046fd0","executionInfo":{"status":"ok","timestamp":1588458480858,"user_tz":-120,"elapsed":998,"user":{"displayName":"Michelangiolo Mazzeschi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghg8l8QeAQA6fd1iPVc2f_F0XQpum7zRjrF4R7HRw=s64","userId":"15738155355555420240"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["a.pop('species')\n","a.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>sepal_length</th>\n","      <th>sepal_width</th>\n","      <th>petal_length</th>\n","      <th>petal_width</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>5.154630</td>\n","      <td>3.385617</td>\n","      <td>1.782467</td>\n","      <td>0.352153</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>4.779764</td>\n","      <td>2.726238</td>\n","      <td>1.380548</td>\n","      <td>0.355968</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>5.284047</td>\n","      <td>3.794925</td>\n","      <td>1.731803</td>\n","      <td>0.222145</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>5.159373</td>\n","      <td>3.428795</td>\n","      <td>1.401698</td>\n","      <td>0.338597</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4.947093</td>\n","      <td>3.123861</td>\n","      <td>1.411329</td>\n","      <td>0.164715</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   sepal_length  sepal_width  petal_length  petal_width\n","0      5.154630     3.385617      1.782467     0.352153\n","1      4.779764     2.726238      1.380548     0.355968\n","2      5.284047     3.794925      1.731803     0.222145\n","3      5.159373     3.428795      1.401698     0.338597\n","4      4.947093     3.123861      1.411329     0.164715"]},"metadata":{"tags":[]},"execution_count":173}]},{"cell_type":"markdown","metadata":{"id":"i08bF2wrkowT","colab_type":"text"},"source":["##Distributions by Labels"]},{"cell_type":"code","metadata":{"id":"nYjDuMuZham_","colab_type":"code","outputId":"76c3c859-6cc2-41dc-8dfc-a6ee4446d836","executionInfo":{"status":"ok","timestamp":1588460130231,"user_tz":-120,"elapsed":13506,"user":{"displayName":"Michelangiolo Mazzeschi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghg8l8QeAQA6fd1iPVc2f_F0XQpum7zRjrF4R7HRw=s64","userId":"15738155355555420240"}},"colab":{"base_uri":"https://localhost:8080/","height":265}},"source":["import matplotlib.pyplot as plt\n","import seaborn as sns\n","\n","for n in range(0, 4):\n","  sns.distplot(distributions_combined[names[n]], hist = True, bins = 1000, kde = True, kde_kws = {'linewidth': 0})\n","#plt.legend(prop={'size': 16}, title = 'Iris')\n","#plt.title('')\n","#plt.xlabel('')\n","#plt.ylabel('')\n","#density_airlines.py hosted with ❤ by GitHub"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"nOKnw2XlkjKl","colab_type":"code","outputId":"ebb25a6b-13c0-41ee-f202-3fbfb5a2df97","executionInfo":{"status":"ok","timestamp":1588459698274,"user_tz":-120,"elapsed":8645,"user":{"displayName":"Michelangiolo Mazzeschi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghg8l8QeAQA6fd1iPVc2f_F0XQpum7zRjrF4R7HRw=s64","userId":"15738155355555420240"}},"colab":{"base_uri":"https://localhost:8080/","height":333}},"source":["for n in range(4, 8):\n","  sns.distplot(distributions_combined[names[n]], hist = True, bins = 1000, kde = True, kde_kws = {'linewidth': 0})"],"execution_count":0,"outputs":[{"output_type":"stream","text":["4\n","5\n","6\n","7\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"HmIH42nYklCE","colab_type":"code","outputId":"3aad3c87-27d1-4688-e6bd-f1d9851e7f7d","executionInfo":{"status":"ok","timestamp":1588459729740,"user_tz":-120,"elapsed":8521,"user":{"displayName":"Michelangiolo Mazzeschi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghg8l8QeAQA6fd1iPVc2f_F0XQpum7zRjrF4R7HRw=s64","userId":"15738155355555420240"}},"colab":{"base_uri":"https://localhost:8080/","height":265}},"source":["for n in range(8, 12):\n","  sns.distplot(distributions_combined[names[n]], hist = True, bins = 1000, kde = True, kde_kws = {'linewidth': 0})"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"XV_NK03gnb7i","colab_type":"text"},"source":["##Distributions by Features"]},{"cell_type":"code","metadata":{"id":"IPG-aIynnRCp","colab_type":"code","outputId":"f7ad4950-777d-4e31-9f15-4c8bc89a92ab","executionInfo":{"status":"ok","timestamp":1588460685470,"user_tz":-120,"elapsed":6904,"user":{"displayName":"Michelangiolo Mazzeschi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghg8l8QeAQA6fd1iPVc2f_F0XQpum7zRjrF4R7HRw=s64","userId":"15738155355555420240"}},"colab":{"base_uri":"https://localhost:8080/","height":265}},"source":["sns.distplot(distributions_combined[names[0]], hist = True, bins = 1000, kde = True, kde_kws = {'linewidth': 0})\n","sns.distplot(distributions_combined[names[4]], hist = True, bins = 1000, kde = True, kde_kws = {'linewidth': 0})\n","sns.distplot(distributions_combined[names[8]], hist = True, bins = 1000, kde = True, kde_kws = {'linewidth': 0})\n","plt.figsize = (20, 20)"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"Rl_pcfywnTPW","colab_type":"code","outputId":"a3dbfd8f-f5db-4453-b5d3-219ccbaf3daf","executionInfo":{"status":"ok","timestamp":1588460371936,"user_tz":-120,"elapsed":6741,"user":{"displayName":"Michelangiolo Mazzeschi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghg8l8QeAQA6fd1iPVc2f_F0XQpum7zRjrF4R7HRw=s64","userId":"15738155355555420240"}},"colab":{"base_uri":"https://localhost:8080/","height":282}},"source":["sns.distplot(distributions_combined[names[1]], hist = True, bins = 1000, kde = True, kde_kws = {'linewidth': 0})\n","sns.distplot(distributions_combined[names[5]], hist = True, bins = 1000, kde = True, kde_kws = {'linewidth': 0})\n","sns.distplot(distributions_combined[names[9]], hist = True, bins = 1000, kde = True, kde_kws = {'linewidth': 0})     "],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"i4xxeX-MnXbY","colab_type":"code","outputId":"ddb97638-6c5d-42fe-80b4-db2e1bd546ec","executionInfo":{"status":"ok","timestamp":1588460377661,"user_tz":-120,"elapsed":10748,"user":{"displayName":"Michelangiolo Mazzeschi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghg8l8QeAQA6fd1iPVc2f_F0XQpum7zRjrF4R7HRw=s64","userId":"15738155355555420240"}},"colab":{"base_uri":"https://localhost:8080/","height":282}},"source":["sns.distplot(distributions_combined[names[2]], hist = True, bins = 1000, kde = True, kde_kws = {'linewidth': 0})\n","sns.distplot(distributions_combined[names[6]], hist = True, bins = 1000, kde = True, kde_kws = {'linewidth': 0})\n","sns.distplot(distributions_combined[names[10]], hist = True, bins = 1000, kde = True, kde_kws = {'linewidth': 0})  "],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 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size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"O4nw8hO2nkFu","colab_type":"code","outputId":"24f8034e-6eb1-4711-ed4d-e92385dcd017","executionInfo":{"status":"ok","timestamp":1588460421200,"user_tz":-120,"elapsed":7368,"user":{"displayName":"Michelangiolo Mazzeschi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghg8l8QeAQA6fd1iPVc2f_F0XQpum7zRjrF4R7HRw=s64","userId":"15738155355555420240"}},"colab":{"base_uri":"https://localhost:8080/","height":282}},"source":["sns.distplot(distributions_combined[names[3]], hist = True, bins = 1000, kde = True, kde_kws = {'linewidth': 0})\n","sns.distplot(distributions_combined[names[7]], hist = True, bins = 1000, kde = True, kde_kws = {'linewidth': 0})\n","sns.distplot(distributions_combined[names[11]], hist = True, bins = 1000, kde = True, kde_kws = {'linewidth': 0})  "],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7fc7bcba70f0>"]},"metadata":{"tags":[]},"execution_count":216},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]}]}